code: def forward(self, x):
      # we could return the latent representation here after the encoder as the latent representation
      x = self.encoder(x)
      x = self.decoder(x)
      return x
  
imports: from __future__ import division
  from __future__ import print_function
  import torch
  import numpy
  from sklearn.preprocessing import StandardScaler
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from pyod.utils.utility import check_parameter
  from pyod.utils.stat_models import pairwise_distances_no_broadcast
  from pyod.models.base import BaseDetector
  from pyod.utils.data import generate_data
  from pyod.utils.stat_models import pairwise_distances_no_broadcast
  from torch import nn
  from torchvision import transforms
label_desc: Use image node_id 2 for calling the inner_autoencoder obj's underlying member method code with example usage: obj.forward(x) and returns: x